DatriseAI-first ETL

JSON File to Amazon DynamoDB Integration

AI-first ETL from JSON File into Amazon DynamoDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads JSON File into Amazon DynamoDB

Datrise syncs JSON File's documents, nested objects, arrays, keys, file batches, and schemas into Amazon DynamoDB as an item per source record in a table per entity. Flexible or custom fields land in nested map/list attributes, and timestamps such as created, updated, and status changes are typed as ISO-8601 string or epoch number attributes.

Sync is incremental: Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id, so re-runs update only what changed. Partition-key design on the entity id to spread throughput evenly. DynamoDB rewards access-pattern-first key design, so Datrise sets partition/sort keys from your entity ids rather than scan-heavy defaults.

Ideal for serverless apps needing single-digit-millisecond key lookups on CRM data.

Endpoints

JSON File: Structured JSON documents from exports and drops.

Amazon DynamoDB: Serverless key-value and document store on AWS.

How JSON File entities map to Amazon DynamoDB

JSON File entityAmazon DynamoDB objectNotes
documentsjson_file_documentsid PK · custom fields → nested map/list attributes
nested objectsjson_file_nested_objectsid PK · linked to json_file_documents
arraysjson_file_arraysid PK · linked to json_file_documents
keysjson_file_keysid PK · linked to json_file_documents

FAQ

How does Datrise handle JSON File's custom fields in Amazon DynamoDB?

Flexible values are stored as nested map/list attributes, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon DynamoDB types.

How does the JSON File to Amazon DynamoDB sync stay up to date?

It runs incrementally — Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id.

Related pipelines

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